Hapsari Peni Agustin, Hanik Badriyah Hidayati, Adri Gabriel Sooai, I. Ketut Eddy Purnama, Mauridhi Hery Purnomo
Volumetric analysis of brain tumors is a decisive thing in the detection of brain tumors to determine the patient's lifetime followed by action to the patient. A few studies had been shown explicitly quantified the brain tumor volume while the analysis of brain tumor volumetric by expert limited with the huge data of brain tumor patient MRI. Thorough the importance of brain tumor analysis in clinical used, the purpose of this research is to evaluate the similarity of a semi-automatic segmentation tool for brain tumor image analysis. The agreement was compared by using differences of means with 95% limits of agreement (LoA). Brain tumor segmentation was obtained by using Fast Marching and Grow Cut segmentation methods. Preoperative MRI images of 20 T2 MRI of low-grade glioma patients from The Cancer Imaging Archive (TCIA) database were used to analyze brain tumor volume. The volume obtained from the two segmentation methods is based on the similarity between the two using the intra-method agreement between two segmentation methods with a 95% limit of agreement (LoA) value and difference volume average of 920 mm3 or 0.92 mL. Its shown that both methods had the same performance. © 2019 IEEE.
Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia; Department of Electrical Engineering, Universitas Negeri Surabaya, Surabaya, Indonesia; Airlangga University, Neurology Department, Surabaya, Indonesia; Universitas Katolik Widya Mandira, Dept. of Computer Science, Kupang, Indonesia; Institut Teknologi Sepuluh Nopember, Dept. of Electrical Engineering, Surabaya, Indonesia; Dept. of Computer Science, Universitas Katolik Widya Mandira, Kupang, Indonesia
